import numpy as np
import os
import gzip
import tensorflow as tf
from PIL import Image

def img_show(img):
    pil_img = Image.fromarray(np.uint8(img))#Image.fromarray影象資料轉換為PIL資料物件
    pil_img.show()#顯示圖片
# 定義載入資料的函式，data_folder為儲存gz資料的資料夾，該資料夾下有4個檔案
# 'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz',
 

# 't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz'

def load_data(data_folder):

  files = [
      'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz',
      't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz'
  ]

  paths = []
  for fname in files:
    paths.append(os.path.join(data_folder,
 
fname))

  with gzip.open(paths[0], 'rb') as lbpath:
    y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)

  with gzip.open(paths[1], 'rb') as imgpath:
    x_train = np.frombuffer(
        imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28)

  with gzip.open(paths[2], 'rb') as
 
 lbpath:
    y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)

  with gzip.open(paths[3], 'rb') as imgpath:
    x_test = np.frombuffer(
        imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28)

  return (x_train, y_train), (x_test, y_test)

(train_images, train_labels), (test_images, test_labels) = load_data('C:\\Users\\Administrator\\MNIST_data\\')

img = train_images[0]#訓練影象賦給img
label = train_labels[0]#訓練標籤賦給label
print(label)  # 5

print(img.shape)  # (784,)
img = img.reshape(28, 28)  # 把影象的形狀變為原來的尺寸
print(img.shape)  # (28, 28)

img_show(img)#函式